Advances in autonomy offer the potential for dramatic positive outcomes in a number of domains, yet enabling their safe deployment remains an open problem. This work's motivating question is: In safety-critical settings, can we avoid the need to have one human supervise one machine at all times? The work formalizes this scalable supervision problem by considering remotely located human supervisors and investigating how autonomous agents can cooperate to achieve safety. This article focuses on the safety-critical context of autonomous vehicles (AVs) merging into traffic consisting of a mixture of AVs and human drivers. The analysis establishes high reliability upper bounds on human supervision requirements. It further shows that AV cooperation can improve supervision reliability by orders of magnitude and counterintuitively requires fewer supervisors (per AV) as more AVs are adopted. These analytical results leverage queuing-theoretic analysis, order statistics, and a conservative, reachability-based approach. A key takeaway is the potential value of cooperation in enabling the deployment of autonomy at scale. While this work focuses on AVs, the scalable supervision framework may be of independent interest to a broader array of autonomous control challenges.

翻译：自主技术的进步在多个领域展现出巨大潜力，但其安全部署仍是一个待解决的开放性问题。本研究的核心问题是：在安全关键场景下，能否避免需要一名人类监督员持续监督一台机器的模式？本文通过考虑远程人类监督员，并探究自主代理如何通过合作实现安全性，正式构建了可扩展监督问题。研究聚焦于包含自主车辆（AV）与人类驾驶员的混合交通环境，分析表明人类监督需求存在高可靠性上限。进一步发现，AV之间的合作可将监督可靠性提升数个数量级，且反直觉的是，随着AV数量增加，每台AV所需监督员反而减少。这些分析结果基于排队论分析、顺序统计量及一种保守的可达性方法。核心启示在于合作在实现大规模自主系统部署中的潜在价值。尽管本文以AV为研究对象，但该可扩展监督框架对更广泛的自主控制挑战亦具有独立参考意义。